Source code for openquake.commonlib.util

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2023 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import numpy
from openquake.baselib import config, python3compat
from openquake.commonlib import datastore

F32 = numpy.float32
read = datastore.read


[docs]def max_rel_diff(curve_ref, curve, min_value=0.01): """ Compute the maximum relative difference between two curves. Only values greather or equal than the min_value are considered. >>> curve_ref = [0.01, 0.02, 0.03, 0.05, 1.0] >>> curve = [0.011, 0.021, 0.031, 0.051, 1.0] >>> round(max_rel_diff(curve_ref, curve), 2) 0.1 """ assert len(curve_ref) == len(curve), (len(curve_ref), len(curve)) assert len(curve), 'The curves are empty!' max_diff = 0 for c1, c2 in zip(curve_ref, curve): if c1 >= min_value: max_diff = max(max_diff, abs(c1 - c2) / c1) return max_diff
[docs]def max_rel_diff_index(curve_ref, curve, min_value=0.01): """ Compute the maximum relative difference between two sets of curves. Only values greather or equal than the min_value are considered. Return both the maximum difference and its location (array index). >>> curve_refs = [[0.01, 0.02, 0.03, 0.05], [0.01, 0.02, 0.04, 0.06]] >>> curves = [[0.011, 0.021, 0.031, 0.051], [0.012, 0.022, 0.032, 0.051]] >>> max_rel_diff_index(curve_refs, curves) (0.2, 1) """ assert len(curve_ref) == len(curve), (len(curve_ref), len(curve)) assert len(curve), 'The curves are empty!' diffs = [max_rel_diff(c1, c2, min_value) for c1, c2 in zip(curve_ref, curve)] maxdiff = max(diffs) maxindex = diffs.index(maxdiff) return maxdiff, maxindex
[docs]def rmsep(array_ref, array, min_value=0): """ Root Mean Square Error Percentage for two arrays. :param array_ref: reference array :param array: another array :param min_value: compare only the elements larger than min_value :returns: the relative distance between the arrays >>> curve_ref = numpy.array([[0.01, 0.02, 0.03, 0.05], ... [0.01, 0.02, 0.04, 0.06]]) >>> curve = numpy.array([[0.011, 0.021, 0.031, 0.051], ... [0.012, 0.022, 0.032, 0.051]]) >>> str(round(rmsep(curve_ref, curve, .01), 5)) '0.11292' """ bigvalues = array_ref > min_value reldiffsquare = (1. - array[bigvalues] / array_ref[bigvalues]) ** 2 return numpy.sqrt(reldiffsquare.mean())
[docs]def log(array, cutoff): """ Compute the logarithm of an array with a cutoff on the small values """ arr = numpy.copy(array) arr[arr < cutoff] = cutoff return numpy.log(arr)
[docs]def closest_to_ref(arrays, ref, cutoff=1E-12): """ :param arrays: a sequence of arrays :param ref: the reference array :returns: a list of indices ordered by closeness This function is used to extract the realization closest to the mean in disaggregation. For instance, if there are 2 realizations with indices 0 and 1, the first hazard curve having values >>> c0 = numpy.array([.99, .97, .5, .1]) and the second hazard curve having values >>> c1 = numpy.array([.98, .96, .45, .09]) with weights 0.6 and 0.4 and mean >>> mean = numpy.average([c0, c1], axis=0, weights=[0.6, 0.4]) then calling ``closest_to_ref`` will returns the indices 0 and 1 respectively: >>> closest_to_ref([c0, c1], mean) [0, 1] This means that the realization 0 is the closest to the mean, as expected, since it has a larger weight. You can check that it is indeed true by computing the sum of the quadratic deviations: >>> ((c0 - mean)**2).sum() 0.0004480000000000008 >>> ((c1 - mean)**2).sum() 0.0010079999999999985 If the 2 realizations have equal weights the distance from the mean will be the same. In that case both the realizations will be okay; the one that will be chosen by ``closest_to_ref`` depends on the magic of floating point approximation (theoretically identical distances will likely be different as numpy.float64 numbers) or on the magic of Python ``list.sort``. """ dist = numpy.zeros(len(arrays)) logref = log(ref, cutoff) pairs = [] for idx, array in enumerate(arrays): diff = log(array, cutoff) - logref dist = numpy.sqrt((diff * diff).sum()) pairs.append((dist, idx)) pairs.sort() return [idx for dist, idx in pairs]
[docs]def compose_arrays(a1, a2, firstfield='etag'): """ Compose composite arrays by generating an extended datatype containing all the fields. The two arrays must have the same length. """ assert len(a1) == len(a2), (len(a1), len(a2)) if a1.dtype.names is None and len(a1.shape) == 1: # the first array is not composite, but it is one-dimensional a1 = numpy.array(a1, numpy.dtype([(firstfield, a1.dtype)])) fields1 = [(f, a1.dtype.fields[f][0]) for f in a1.dtype.names] if a2.dtype.names is None: # the second array is not composite assert len(a2.shape) == 2, a2.shape width = a2.shape[1] fields2 = [('value%d' % i, a2.dtype) for i in range(width)] composite = numpy.zeros(a1.shape, numpy.dtype(fields1 + fields2)) for f1 in dict(fields1): composite[f1] = a1[f1] for i in range(width): composite['value%d' % i] = a2[:, i] return composite fields2 = [(f, a2.dtype.fields[f][0]) for f in a2.dtype.names] composite = numpy.zeros(a1.shape, numpy.dtype(fields1 + fields2)) for f1 in dict(fields1): composite[f1] = a1[f1] for f2 in dict(fields2): composite[f2] = a2[f2] return composite
[docs]def get_assets(dstore): """ :param dstore: a datastore with keys 'assetcol' :returns: an array of records (id, tag1, ..., tagN, lon, lat) """ assetcol = dstore['assetcol'] tagnames = sorted(tn for tn in assetcol.tagnames if tn != 'id') if 'site_id' in tagnames: # special case, starts from 1 and not from 0 assetcol.array['site_id'] += 1 tag = {t: getattr(assetcol.tagcol, t) for t in tagnames} dtlist = [('id', '<S100')] for tagname in tagnames: dtlist.append((tagname, '<S100')) dtlist.extend([('lon', F32), ('lat', F32)]) asset_data = [] for a in assetcol.array: tup = tuple(python3compat.encode(tag[t][a[t]]) for t in tagnames) asset_data.append((a['id'],) + tup + (a['lon'], a['lat'])) return numpy.array(asset_data, dtlist)
[docs]def shared_dir_on(): """ :returns: True if a shared_dir has been set in openquake.cfg, else False """ return config.directory.shared_dir